Abstract: Stock price prediction is extremely difficult owing to irregularity in stock prices. Because stock price sometimes shows similar patterns and is determined by a variety of factors, we present a novel concept of finding similar patterns in historical stock data for high-accuracy daily stock price prediction with potential rules for simultaneously selecting the main factors that have a significant effect on the stock price. Our objective is to propose a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use hierarchical clustering to easily find similar patterns in the layer adjacent to the current pattern according to the hierarchical structure. Second, we select the determinants that are most influenced by the stock price using feature selection. Moreover, we generate an artificial neural network model that provides numerous opportunities for predicting the best stock price. Finally, to verify the validity of our model, we use the root mean square error (RMSE) as a measure of prediction accuracy. The forecasting results show that the proposed model can achieve high prediction accuracy for each stock by using this measure.(More)

Stock price prediction is extremely difficult owing to irregularity in stock prices. Because stock price sometimes shows similar patterns and is determined by a variety of factors, we present a novel concept of finding similar patterns in historical stock data for high-accuracy daily stock price prediction with potential rules for simultaneously selecting the main factors that have a significant effect on the stock price. Our objective is to propose a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use hierarchical clustering to easily find similar patterns in the layer adjacent to the current pattern according to the hierarchical structure. Second, we select the determinants that are most influenced by the stock price using feature selection. Moreover, we generate an artificial neural network model that provides numerous opportunities for predicting the best stock price. Finally, to verify the validity of our model, we use the root mean square error (RMSE) as a measure of prediction accuracy. The forecasting results show that the proposed model can achieve high prediction accuracy for each stock by using this measure.

@conference{iotbd16,author={Seungwoo Jeon and Bonghee Hong and Hyun-jik Lee and Juhyeong Kim},title={Stock Price Prediction based on Stock Big Data and Pattern Graph Analysis},booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,},year={2016},pages={223-231},publisher={SciTePress},organization={INSTICC},doi={10.5220/0005876102230231},isbn={978-989-758-183-0},}